@article{2210e5f352614df7882024d1a5bd4b8c,
title = "An ISS-modular approach for adaptive neural control of pure-feedback systems",
abstract = "Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback system using radial basis function (RBF) neural networks (NN). An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem. The difficulty in controlling the non-affine pure-feedback system is overcome by achieving the so-called {"}ISS-modularity{"} of the controller-estimator. Specifically, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors, and a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. The stability of the entire closed-loop system is guaranteed by the small-gain theorem. The ISS-modular approach provides an effective way for controlling non-affine non-linear systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach.",
keywords = "Adaptive neural control, Input-to-state stability, Non-affine systems, Pure-feedback systems, Small-gain theorem",
author = "Cong Wang and Hill, \{David J.\} and Ge, \{S. S.\} and Guanrong Chen",
note = "Funding Information: Cong Wang received B.E. and M.E. degrees from Department of Automatic Control, Beijing University of Aeronautic \& Astronautics, China, in 1989 and 1997, respectively, and the Ph.D. degree from the Department of Electrical \& Computer Engineering, the National University of Singapore in 2002. From 2001 to 2004, he did his postdoctoral research at the Department of Electronic Engineering, City University of Hong Kong. He has been with the College of Automation, the South China University of Technology since 2004, where he is currently a Professor. He has authored and co-authored over 30 international journal and conference papers. He is presently serving as an Associate Editor of IEEE Control Systems Society Conference Editorial Board. From May 2005, he serves as a program director at the Directorates for Information Sciences, the National Natural Science Foundation of China (NSFC). His research interest includes deterministic learning theory, intelligent and autonomous control, dynamical pattern recognition, and cognitive and brain sciences. Funding Information: This research was supported in part by the Hong Kong Research Grant Council under the CERG Grant CityU 1114/05E, and by the Natural Science Foundation of Guangdong Province under Grant no. 05006528. The authors would also thank the anonymous reviewers for the constructive comments which helps improve the quality and presentation of the paper. ",
year = "2006",
month = may,
doi = "10.1016/j.automatica.2006.01.004",
language = "English",
volume = "42",
pages = "723--731",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier",
number = "5",
}